@inproceedings{wang-etal-2022-1cademy,
    title = "1{C}ademy at {S}emeval-2022 Task 1: Investigating the Effectiveness of Multilingual, Multitask, and Language-Agnostic Tricks for the Reverse Dictionary Task",
    author = "Wang, Zhiyong  and
      Zhang, Ge  and
      Lashkarashvili, Nineli",
    editor = "Emerson, Guy  and
      Schluter, Natalie  and
      Stanovsky, Gabriel  and
      Kumar, Ritesh  and
      Palmer, Alexis  and
      Schneider, Nathan  and
      Singh, Siddharth  and
      Ratan, Shyam",
    booktitle = "Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022)",
    month = jul,
    year = "2022",
    address = "Seattle, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.semeval-1.2",
    doi = "10.18653/v1/2022.semeval-1.2",
    pages = "15--22",
    abstract = "This paper describes our system for the Se- mEval2022 task of matching dictionary glosses to word embeddings. We focus on the Reverse Dictionary Track of the competition, which maps multilingual glosses to reconstructed vector representations. More specifically, models convert the input of sentences to three types of embeddings: SGNS, Char, and Electra. We pro- pose several experiments for applying neural network cells, general multilingual and multi-task structures, and language-agnostic tricks to the task. We also provide comparisons over different types of word embeddings and ablation studies to suggest helpful strategies. Our initial transformer-based model achieves relatively low performance. However, trials on different retokenization methodologies indicate improved performance. Our proposed Elmo- based monolingual model achieves the highest outcome, and its multitask, and multilingual varieties show competitive results as well.",
}